Opinion-Based Entity Ranking using learning to rank

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Shariq Bashir and Wasif Afzal and Abdul Rauf Baig",
  title =        "Opinion-Based Entity Ranking using learning to rank",
  journal =      "Applied Soft Computing",
  volume =       "38",
  pages =        "151--163",
  year =         "2016",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2015.10.001",
  URL =          "http://www.sciencedirect.com/science/article/pii/S156849461500616X",
  abstract =     "As social media and e-commerce on the Internet
                 continue to grow, opinions have become one of the most
                 important sources of information for users to base
                 their future decisions on. Unfortunately, the large
                 quantities of opinions make it difficult for an
                 individual to comprehend and evaluate them all in a
                 reasonable amount of time. The users have to read a
                 large number of opinions of different entities before
                 making any decision. Recently a new retrieval task in
                 information retrieval known as Opinion-Based Entity
                 Ranking (OpER) has emerged. OpER directly ranks
                 relevant entities based on how well opinions on them
                 are matched with a user's preferences that are given in
                 the form of queries. With such a capability, users do
                 not need to read a large number of opinions available
                 for the entities. Previous research on OpER does not
                 take into account the importance and subjectivity of
                 query keywords in individual opinions of an entity.
                 Entity relevance scores are computed primarily on the
                 basis of occurrences of query keywords match, by
                 assuming all opinions of an entity as a single field of
                 text. Intuitively, entities that have positive
                 judgements and strong relevance with query keywords
                 should be ranked higher than those entities that have
                 poor relevance and negative judgments. This paper
                 outlines several ranking features and develops an
                 intuitive framework for OpER in which entities are
                 ranked according to how well individual opinions of
                 entities are matched with the user's query keywords. As
                 a useful ranking model may be constructed from many
                 ranking features, we apply learning to rank approach
                 based on genetic programming (GP) to combine features
                 in order to develop an effective retrieval model for
                 OpER task. The proposed approach is evaluated on two
                 collections and is found to be significantly more
                 effective than the standard OpER approach.",
  keywords =     "genetic algorithms, genetic programming, Entity
                 Ranking, Opinion analysis, Learning to rank",

Genetic Programming entries for Shariq Bashir Wasif Afzal Abdul Rauf Baig